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An automated sleep-state classification algorithm for quantifying sleep timing and sleep-dependent dynamics of electroencephalographic and cerebral metabolic parameters.

Rempe MJ, Clegern WC, Wisor JP - Nat Sci Sleep (2015)

Bottom Line: Automated state scoring can minimize the burden associated with state and thereby facilitate the use of shorter epoch durations.Error associated with mathematical modeling of temporal dynamics of both EEG slow-wave activity and cerebral lactate either did not differ significantly when state scoring was done with automated versus visual scoring, or was reduced with automated state scoring relative to manual classification.Machine scoring is as effective as human scoring in detecting experimental effects in rodent sleep studies.

View Article: PubMed Central - PubMed

Affiliation: Mathematics and Computer Science, Whitworth University, Spokane, WA, USA ; College of Medical Sciences and Sleep and Performance Research Center, Washington State University, Spokane, WA, USA.

ABSTRACT

Introduction: Rodent sleep research uses electroencephalography (EEG) and electromyography (EMG) to determine the sleep state of an animal at any given time. EEG and EMG signals, typically sampled at >100 Hz, are segmented arbitrarily into epochs of equal duration (usually 2-10 seconds), and each epoch is scored as wake, slow-wave sleep (SWS), or rapid-eye-movement sleep (REMS), on the basis of visual inspection. Automated state scoring can minimize the burden associated with state and thereby facilitate the use of shorter epoch durations.

Methods: We developed a semiautomated state-scoring procedure that uses a combination of principal component analysis and naïve Bayes classification, with the EEG and EMG as inputs. We validated this algorithm against human-scored sleep-state scoring of data from C57BL/6J and BALB/CJ mice. We then applied a general homeostatic model to characterize the state-dependent dynamics of sleep slow-wave activity and cerebral glycolytic flux, measured as lactate concentration.

Results: More than 89% of epochs scored as wake or SWS by the human were scored as the same state by the machine, whether scoring in 2-second or 10-second epochs. The majority of epochs scored as REMS by the human were also scored as REMS by the machine. However, of epochs scored as REMS by the human, more than 10% were scored as SWS by the machine and 18 (10-second epochs) to 28% (2-second epochs) were scored as wake. These biases were not strain-specific, as strain differences in sleep-state timing relative to the light/dark cycle, EEG power spectral profiles, and the homeostatic dynamics of both slow waves and lactate were detected equally effectively with the automated method or the manual scoring method. Error associated with mathematical modeling of temporal dynamics of both EEG slow-wave activity and cerebral lactate either did not differ significantly when state scoring was done with automated versus visual scoring, or was reduced with automated state scoring relative to manual classification.

Conclusions: Machine scoring is as effective as human scoring in detecting experimental effects in rodent sleep studies. Automated scoring is an efficient alternative to visual inspection in studies of strain differences in sleep and the temporal dynamics of sleep-related physiological parameters.

No MeSH data available.


Related in: MedlinePlus

Strain differences in EEG power spectra in 10-second epochs scored by human or machine.Notes: The left column shows human-scored data and the right column shows machine-scored data. For wake (A and D), SWS (B and E), and REMS (C and F), EEG power differed between strains at specific frequencies. The black dots at the base of each graph indicate frequency bands in which posthoc comparisons (Fisher’s protected least-significant difference) indicated a difference between the BA (black line) and B6 (gray line) strains. The insets in panels (B and (E) show 1–4 Hz SWA, which did not differ between strains.Abbreviations: B6, C57BL/6J mice; BA, BALB/CJ mice; EEG, electroencephalography; NS, not significant; REMS, rapid-eye-movement sleep; SWS, slow-wave sleep.
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f11-nss-7-085: Strain differences in EEG power spectra in 10-second epochs scored by human or machine.Notes: The left column shows human-scored data and the right column shows machine-scored data. For wake (A and D), SWS (B and E), and REMS (C and F), EEG power differed between strains at specific frequencies. The black dots at the base of each graph indicate frequency bands in which posthoc comparisons (Fisher’s protected least-significant difference) indicated a difference between the BA (black line) and B6 (gray line) strains. The insets in panels (B and (E) show 1–4 Hz SWA, which did not differ between strains.Abbreviations: B6, C57BL/6J mice; BA, BALB/CJ mice; EEG, electroencephalography; NS, not significant; REMS, rapid-eye-movement sleep; SWS, slow-wave sleep.

Mentions: Repeated-measures analysis of variance indicated significant strain × frequency interactions affecting spectral profiles of wake, SWS, and REMS in both human-scored and machine-scored datasets for both 10-second (Table 4) and 2-second epoch data (not shown). Posthoc comparisons of EEG spectral profiles between strains across 1 Hz bins indicated that the strain difference detected in the EEG spectral profile in human scored datasets was replicated in the machine-scored datasets (Figure 11). For instance, in 10-second epochs scored as wake, spectral power in the 9–11 Hz range was elevated in B6 mice relative to BA mice in both the human-scored and machine-scored datasets (Figure 11A and D). In 10-second epochs scored as SWS, spectral power in the 3–12 Hz range was elevated in B6 mice relative to BA mice in both the human-scored and machine-scored datasets (Figure 11B and E). In 10-second epochs scored as REMS, spectral power in the 6–11 Hz range was elevated in B6 mice relative to BA mice in both the human-scored and machine-scored datasets (Figure 11C and F). These data demonstrate that strain differences in EEG oscillatory activity across sleep states were detected by the machine-scoring method. Similarly, the overlap between scoring methods in detecting strain differences in EEG spectral profiles within the 2-second datasets was near total (data not shown).


An automated sleep-state classification algorithm for quantifying sleep timing and sleep-dependent dynamics of electroencephalographic and cerebral metabolic parameters.

Rempe MJ, Clegern WC, Wisor JP - Nat Sci Sleep (2015)

Strain differences in EEG power spectra in 10-second epochs scored by human or machine.Notes: The left column shows human-scored data and the right column shows machine-scored data. For wake (A and D), SWS (B and E), and REMS (C and F), EEG power differed between strains at specific frequencies. The black dots at the base of each graph indicate frequency bands in which posthoc comparisons (Fisher’s protected least-significant difference) indicated a difference between the BA (black line) and B6 (gray line) strains. The insets in panels (B and (E) show 1–4 Hz SWA, which did not differ between strains.Abbreviations: B6, C57BL/6J mice; BA, BALB/CJ mice; EEG, electroencephalography; NS, not significant; REMS, rapid-eye-movement sleep; SWS, slow-wave sleep.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4562753&req=5

f11-nss-7-085: Strain differences in EEG power spectra in 10-second epochs scored by human or machine.Notes: The left column shows human-scored data and the right column shows machine-scored data. For wake (A and D), SWS (B and E), and REMS (C and F), EEG power differed between strains at specific frequencies. The black dots at the base of each graph indicate frequency bands in which posthoc comparisons (Fisher’s protected least-significant difference) indicated a difference between the BA (black line) and B6 (gray line) strains. The insets in panels (B and (E) show 1–4 Hz SWA, which did not differ between strains.Abbreviations: B6, C57BL/6J mice; BA, BALB/CJ mice; EEG, electroencephalography; NS, not significant; REMS, rapid-eye-movement sleep; SWS, slow-wave sleep.
Mentions: Repeated-measures analysis of variance indicated significant strain × frequency interactions affecting spectral profiles of wake, SWS, and REMS in both human-scored and machine-scored datasets for both 10-second (Table 4) and 2-second epoch data (not shown). Posthoc comparisons of EEG spectral profiles between strains across 1 Hz bins indicated that the strain difference detected in the EEG spectral profile in human scored datasets was replicated in the machine-scored datasets (Figure 11). For instance, in 10-second epochs scored as wake, spectral power in the 9–11 Hz range was elevated in B6 mice relative to BA mice in both the human-scored and machine-scored datasets (Figure 11A and D). In 10-second epochs scored as SWS, spectral power in the 3–12 Hz range was elevated in B6 mice relative to BA mice in both the human-scored and machine-scored datasets (Figure 11B and E). In 10-second epochs scored as REMS, spectral power in the 6–11 Hz range was elevated in B6 mice relative to BA mice in both the human-scored and machine-scored datasets (Figure 11C and F). These data demonstrate that strain differences in EEG oscillatory activity across sleep states were detected by the machine-scoring method. Similarly, the overlap between scoring methods in detecting strain differences in EEG spectral profiles within the 2-second datasets was near total (data not shown).

Bottom Line: Automated state scoring can minimize the burden associated with state and thereby facilitate the use of shorter epoch durations.Error associated with mathematical modeling of temporal dynamics of both EEG slow-wave activity and cerebral lactate either did not differ significantly when state scoring was done with automated versus visual scoring, or was reduced with automated state scoring relative to manual classification.Machine scoring is as effective as human scoring in detecting experimental effects in rodent sleep studies.

View Article: PubMed Central - PubMed

Affiliation: Mathematics and Computer Science, Whitworth University, Spokane, WA, USA ; College of Medical Sciences and Sleep and Performance Research Center, Washington State University, Spokane, WA, USA.

ABSTRACT

Introduction: Rodent sleep research uses electroencephalography (EEG) and electromyography (EMG) to determine the sleep state of an animal at any given time. EEG and EMG signals, typically sampled at >100 Hz, are segmented arbitrarily into epochs of equal duration (usually 2-10 seconds), and each epoch is scored as wake, slow-wave sleep (SWS), or rapid-eye-movement sleep (REMS), on the basis of visual inspection. Automated state scoring can minimize the burden associated with state and thereby facilitate the use of shorter epoch durations.

Methods: We developed a semiautomated state-scoring procedure that uses a combination of principal component analysis and naïve Bayes classification, with the EEG and EMG as inputs. We validated this algorithm against human-scored sleep-state scoring of data from C57BL/6J and BALB/CJ mice. We then applied a general homeostatic model to characterize the state-dependent dynamics of sleep slow-wave activity and cerebral glycolytic flux, measured as lactate concentration.

Results: More than 89% of epochs scored as wake or SWS by the human were scored as the same state by the machine, whether scoring in 2-second or 10-second epochs. The majority of epochs scored as REMS by the human were also scored as REMS by the machine. However, of epochs scored as REMS by the human, more than 10% were scored as SWS by the machine and 18 (10-second epochs) to 28% (2-second epochs) were scored as wake. These biases were not strain-specific, as strain differences in sleep-state timing relative to the light/dark cycle, EEG power spectral profiles, and the homeostatic dynamics of both slow waves and lactate were detected equally effectively with the automated method or the manual scoring method. Error associated with mathematical modeling of temporal dynamics of both EEG slow-wave activity and cerebral lactate either did not differ significantly when state scoring was done with automated versus visual scoring, or was reduced with automated state scoring relative to manual classification.

Conclusions: Machine scoring is as effective as human scoring in detecting experimental effects in rodent sleep studies. Automated scoring is an efficient alternative to visual inspection in studies of strain differences in sleep and the temporal dynamics of sleep-related physiological parameters.

No MeSH data available.


Related in: MedlinePlus